System and method for feature extraction from real-time vehicle kinetics data for remote tire wear modeling

A method for estimating vehicle tire wear, the method comprising:

• via one or more sensors associated with a vehicle and/or at least one tire of a plurality of tires supporting the vehicle, generating first data corresponding to real-time kinetics of the vehicle and/or the at least one tire;
• via a computing system onboard the vehicle, processing the first data to generate second data as a reduced subset of the first data, said second data comprising one or more predetermined features extracted therefrom;
• via a global positioning system (GPS) transceiver, generating GPS data corresponding to vehicle positions;
• selectively transmitting the second data and the GPS data to a remote computing system via a communications network;
• via the remote computing system, processing the second data and the GPS data further in view of a vehicle model and one or more vehicle route characteristics to generate third data corresponding to the first data, and further processing the third data to estimate a current tire wear characteristic for the at least one tire;
• and
• selectively providing real-time feedback based on the estimated current tire wear characteristic the second data is generated via an encoding neural network layer, the third data is generated via a decoding neural network layer, and a wear calculation layer is appended to the output of the decoding neural network layer and configured to transform decoded signals into instantaneous estimated wear values for the at least one tire, a training process for the neural network layers wherein comparing the estimated wear values are compared to actual wear values for the at least one tire to generate an error value, and providing the error value as feedback to the neural network layers.

A method for estimating vehicle tire wear, the method comprising:

• via one or more sensors associated with a vehicle and/or at least one tire of a plurality of tires supporting the vehicle, generating first data corresponding to real-time kinetics of the vehicle and/or the at least one tire;
• processing the first data, via a computing system onboard the vehicle, to generate second data as a reduced subset of the first data, said second data representative of the first data and comprising any one or more predetermined features extracted therefrom, wherein the second data (930) is generated via an encoding neural network layer;
• selectively transmitting the second data to a remote computing system via a communications network;
• and
• via the remote computing system, processing the second data to generate third data corresponding to the first data via a decoding neural network layer, processing the third data to estimate a wear characteristic for the at least one tire, and generating a notification associated with the estimated wear characteristic to a computing device associated with a vehicle user the second data is generated via an encoding neural network layer, the third data is generated via a decoding neural network layer, and a wear calculation layer is appended to the output of the decoding neural network layer and configured to transform decoded signals into instantaneous estimated wear values for the at least one tire, a training process for the neural network layers wherein comparing the estimated wear values are compared to actual wear values for the at least one tire to generate an error value, and providing the error value as feedback to the neural network layers.